Big data engineering has been chosen by DICE as the profession that will grow the quickest by 2020, with a growth rate of 50% between 2015 and 2020. Because of the huge demand for them, the competition among businesses of all kinds for them, and the rising compensation, now is a great moment to work as a data engineer. The information that follows is a comprehensive introduction to a career in big data engineering and will provide you with all the information you need to get started.
Big Data Engineering: What Is It?
The term “big data,” which describes the enormous amounts of customer information and transaction records generated by websites and services as varied as The New York Times and cloud storage providers like Amazon and Facebook, is undoubtedly already recognizable to you. Big data is so vast and diverse that it is hard for humans to analyse it and form inferences from it properly. Where things become fascinating is in big data engineering.
The creation of systems for gathering and making sense of enormous volumes of data, such as the millions or even billions of clicks, taps, likes, swipes, shares, and purchases that companies and consumers make every day, is the focus of the discipline of big data engineering. By building data pipelines and concentrating on the ETL (Extract, Transform, and Load) process, as well as by creating and maintaining data infrastructures like big data frameworks and databases, they are able to do this.
Can You Describe a Big Data Engineer’s Role?
Big data engineers work with the data processing infrastructure, thus it’s useful to think of them as data architects. They are in charge of developing, preserving, and enhancing the system that accomplishes the task. To do this, big data engineers need to be knowledgeable in both SQL and NoSQL databases as well as database systems like Cassandra, Bigtable, and Hadoop.
These skills enable big data engineers to build and manage data processes that other data experts, such as data scientists and analysts, may utilize to test hypotheses and analyze data. The conversion of huge data into useful information is facilitated by data engineers.
Understanding the Big Data Engineer’s Function
Software developers are large data engineers, sometimes referred to as data engineers, when it comes to big data. Although the precise responsibilities of a data engineer may differ from one firm to the next, the essential skills and degrees of expertise are usually the same.
The Role of the Big Data Engineer
Massive volumes of raw data must be transformed by data engineers into more manageable formats so that they may be processed and analyzed further. For this work, proficiency in a wide range of technological domains, including SQL and AWS, is necessary. For example, the kinds of programming languages a data engineer must be familiar with, the company’s preferred data storage options, and background information on the teams the data engineer will work with are all things that will normally be included in the job description.
Job advertisements for data engineers state that ideal candidates should have knowledge of:
- Build and maintain a superior data pipeline.
- Create and keep a database management system current.
- huge data volumes should be gathered and organized to meet organizational demands.
- Find, develop, and implement strategies to enhance our own internal operations.
- By simplifying data transmission and changing the underlying architecture, scalability may be increased.
- Create a framework utilizing SQL and Amazon Web Services for effective data extraction, transformation, and loading from a variety of sources.
- Develop analytical tools that utilize the data pipeline to deliver insightful data on customer acquisition, operational effectiveness, and other important company performance metrics.
- Collaborate with internal and external stakeholders to address data infrastructure needs and help resolve technical challenges linked to data.
- Create data tools for your analytical and data science team members.
Engineering Duties Related to Big Data
Any business needs engineers that are data-focused since they are the ones who design and manage the systems used to store and process information. Data engineers frequently have to transform raw data into a more understandable format for analytical reasons.
The first step is to clean up the data by removing duplicate entries and organizing the data. Data pipelines are a term used in computer science to describe the movement and storage of data. In order to evaluate the raw data from a SaaS platform, such as a CRM system or email marketing tool, it is first stored in a data warehouse.
How Much Will Hiring a Big Data Engineer Cost?
Big data engineers’ salaries have increased significantly as a result of the demand for their services. Data engineers are said to make above-average salaries according to Hired’s 2019 Salary Guide, which also includes salary statistics for software engineers. In New York City, a data engineer makes on average $132,000 a year. This amount increases to $151,000 in San Francisco.
The typical starting wage for a data engineer is $97,000, according to ZipRecruiter.
Future Opportunities for Data Engineers
There is no set sequence of steps to follow in order to become a great data engineer because the discipline is new. Similar to many other technical professionals, data engineers typically start out with a bachelor’s degree in computer science, applied mathematics, statistics, or a closely related field before continuing their education with courses in programming languages, information technology, or data analytics.
These skills and qualifications will be used by aspiring data engineers to apply for entry-level data engineering jobs or to land positions in other departments that will allow them to further their careers. Because they may learn the most about a company’s data demands and the procedures involved in acquiring, organizing, and exploiting that data in IT, data engineers frequently begin their careers there.
How Does One Navigate the Professional Waters of Big Data Engineering?
There are many different job descriptions for data engineers. The scope of these jobs is significantly influenced by the amount of data gathered, the size of the firm, and the complexity of its data operations.
- A data engineer may be in charge of everything from setting up data sources to managing analytics tools in smaller firms. They would therefore be in charge of database design, data pipeline development, and data warehouse administration, among other components of a data science project.
- Data scientists and data engineers are present in medium-sized businesses, and they collaborate to develop the custom software solutions necessary to meet certain big data analytics goals. They oversee the data integration tool architecture that transfers data between many data sources and a central data repository. Both simple information transmission and more complicated uses are possible with these pipelines.
- In a big firm, building table schemas, populating analytics databases, and optimizing them for quick analysis are typical tasks for a data engineer. The ETL process requires that data be obtained from diverse sources, converted into a format that is simple to evaluate, and then fed into a data warehouse.
Steps to Taking the Big Data Engineer Exam
Big data engineers with suitable experience and education, who can demonstrate their skill with the required technologies, and who can flourish in the face of change are preferred by hiring managers. Below are examples of typical credentials seen in job descriptions for data engineers.
Education
It is not necessary to have a degree in computer science, mathematics, statistics, physics, or a related discipline to work as a data engineer, but the majority of people in the profession do. You may want to enroll in a course or bootcamp that teaches both the technical skills you’ll need and the analytical and critical thinking skills that will help you adapt to new situations if you don’t already have a degree in data engineering or one in a related field but still want to work in the field.
Skills
A thorough understanding of several programming languages, automation and scripting tools, database management systems, data processing, and cloud computing is required for the highly specialized area of data engineering.
Data engineers must have knowledge of a wide range of topics, including data warehousing, ETL, data APIs, machine learning, the principles of distributed systems, as well as collaboration and interpersonal skills.
Qualifications
A bachelor’s degree is sufficient for entry-level work in data engineering, but candidates can stand out and reassure hiring managers that they have experience with industry tools and best practices by obtaining additional credentials, such as vendor-specific certifications or a more general Certified Data Management certificate. Furthermore, certifications from IBM, Cloudera, Microsoft, and Oracle are accessible.
Essential Languages and Technologies
Along with programming languages like Python, SQL, R, C++, and Java, data engineers employ a variety of platforms and technologies linked to data science and data analytics, including Apache MapReduce, MongoDB, Scala, Cloudera, Amazon Web Services, Azure, and Perl are some of the technologies used in Spark.
Advice for Aspiring Big Data Engineers
- Take a Course
- Get Certified
- Build a Portfolio
- Start From the Bottom
- Use Your Time Wisely and Focus on the Most Useful Project You Can
- Network Like Crazy
There are several methods to start a career in data engineering. The average route taken by those who have been successful in landing jobs in big data engineering is shown below.
1. Take a Course
Software engineers with a focus on data analysis and statistics are known as data engineers. You should brush up on your programming knowledge and become acquainted with languages like Python, SQL, and R if you want to become a data engineer. Through participation in an online course or bootcamp, one may learn the foundations of data science, including analysis, statistics, working knowledge of data pipelines, frameworks, and architectures, as well as the most widely used data management and storage technologies.
2. Get Certified
To offer yourself a competitive edge in the employment market, think about being certified in data management. This will not only show prospective employers that you are committed to keeping up with industry advances, but it will also teach you more about subjects like business intelligence and warehouse systems, data ethics and governance, data security, and metadata management. The Global Data Management Community offers certificates in data management, while organizations like Microsoft, Oracle, and IBM offer vendor-specific certifications.
3. Build a Portfolio
Possessing a strong body of work in your portfolio might make you stand out to potential employers even if you lack a degree or years of relevant experience. Whether you’ve worked on projects in your own time or carried out some of the responsibilities of the position while serving in another capacity, a portfolio may demonstrate to potential employers that you have the knowledge and abilities required to be a big data engineer.
4. Start From the Bottom
It can be difficult to find an entry-level job in data engineering; as a result, you might want to think about doing an internship in the industry or starting your career in a related field that will expose you to the difficulties and help you acquire the skills required to be successful as a big data engineer. For example, beginning as an IT analyst will educate you SQL and data warehousing, enable you to build data pipelines, and open the door for a lateral shift.
5. Make Progress on Any Appropriate Task You Can
It’s common to think that the only way to gain experience in data engineering is to work in that field. This is untrue, as data engineers possess many of the same skills as software engineers, data analysts and scientists, quality assurance engineers, and other IT professionals within an organization; as a result, data engineering bootcamps give students practical experience working on real-world projects; and as a result, nothing prevents someone from compiling and transforming publicly accessible datasets.
6. Network Like Crazy
Big data engineers frequently learn about new employment opportunities via referrals from their peers. In light of this, it is beneficial to build a network of industry colleagues and mentors as well as professional allies. If you are now enrolled in a bootcamp or an online school, make use of the network of mentors and career counselors that are readily available to you. Make an attempt to connect with higher management and the company’s data engineers. And constantly keep an eye out for conferences and other networking opportunities in your industry.
The Big Data Engineer’s Frequently Asked Questions
Do you still have questions regarding how to become a big data engineer? Check out the information we have available to answer your issues.
How challenging is it to break into the Big Data Engineering field?
Big data engineering is a highly technical sector that necessitates fluency in a variety of languages, in-depth understanding of database design, and the capacity to stay up with cutting-edge technologies and data warehousing solutions. As a result, becoming one is not an easy task.
Despite the difficulties, those who can think critically, solve problems analytically, and desire to significantly impact their companies will find the education and work satisfying. This is especially true given the field’s fast development and expanding significance across all industries.
Is a Degree in Higher Education Required to Become a Data Engineer?
A bachelor’s degree in math, statistics, computer science, or a relevant business subject is desired but not necessary. The only prerequisite is passing an online bootcamp or course that covers the principles of advanced statistics and programming languages helpful for data mining, querying, and, in certain circumstances, making use of big data SQL engines.
On the other side, data engineers are skilled software engineers who are knowledgeable about database design and the creation of data pipelines. It’s still difficult to find a university course that addresses this, therefore a self-paced online bootcamp in data science or data engineering is a preferable choice. The primary languages used by data engineers are Python, R, and SQL, and you will study them as well as machine learning and data pipeline construction here.
Must a Big Data Engineer Have Coding Experience?
Unfortunately, it is unavoidable that knowledge of Python, SQL, and Java is necessary for success in the field of big data engineering. A growing number of data engineering bootcamps provide quick, self-paced courses to expose total beginners to the programming languages needed to benefit from a more advanced data engineering bootcamp if learning to code sounds daunting.
It is spoken about how long it takes to train as a Big Data engineer.
The length of time it takes to become a big data engineer varies greatly based on things including the candidate’s place of education and whether or not they worked in a relevant industry prior to changing careers. A 6-month online bootcamp may teach students all they need to know to succeed in the profession of big data engineer with a weekly study time commitment of 15-20 hours.
Can You Work Your Way Up to Big Data Engineer Without Prior Experience?
Although it is possible to enter the area of data engineering without any experience, candidates’ chances of landing a job are increased by participating in internships or working in a position that requires data engineering skills.
The most crucial thing is to get any real data engineering experience you can, even if it isn’t stated on your CV, so that you can show potential employers through projects and case studies that you have what it takes to accomplish the job.
